A quick commerce performance marketing engine
Where should the next marketing rupee go, by platform, SKU and city? Agents decide, the team approves.
A fast growing beverage brandProblem statement
What the client was up against
The brand sells across six quick commerce platforms, hundreds of SKUs and many cities. Spend decisions relied on platform reports that never lined up, so budget went where it was easiest, not where it worked.
Our approach
How we approached it
- Pulled signals from every quick commerce platform into one view
- Modelled return by platform, SKU and city
- Let the team approve plans before the system executes
- Learned from every result
Solution we created
What we built
A Cortex marketing engine that turns platform signals into an investment plan. The team approves, the system executes and learns.
Agents in the system
The multi agent team behind it
SourcesQuick commerce platforms, ad accounts, sales data
Signal agentCollects sales, rank and ad data by platform
Forecast agentPredicts return by SKU and city
Planner agentBuilds the spend plan
Learning agentTunes the plan from results
People decideReview, approve and act
Signal agentCollects sales, rank and ad data by platform
Forecast agentPredicts return by SKU and city
Planner agentBuilds the spend plan
Learning agentTunes the plan from results
Quick commerce platformsad accountssales data
What it was built to change
The outcome it is designed for
Marketing spend that follows return, not habit, with one view across every platform.
This is a prototype without published results yet. We show the problem and the build, not numbers we have not measured.
Client names, detailed case studies, demos and references can be shared on request.
Looking for a similar solution?
Tell us your version of this problem. We will walk you through what we built and what a first pilot would look like for you.
